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Paper Citation Record · LEDGER

Ultra-Sparse Memory Network

As of 16 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 9 inbound Pith citation observations for arXiv:2411.12364.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2411.12364 v2

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T17:40:11.880130Z

measured 55 of 55 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T12:03:42.481591Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-04T01:09:19.454543Z

Reference resolution

46 of 46 outbound references displayed

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External citation measurements

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Outbound references

Observation 49dd7439-157b-4e5c-a064-76f8c89ed5ba · outbound

This paper cites Singular value decomposition (svd) and generalized singular value decomposition.

Ultra-Sparse Memory Network Singular value decomposition (svd) and generalized singular value decomposition

Reference 1

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source=arxiv_source observed=2026-08-12T17:40:11.713352Z digest=sha256:0eb24244c99d66f0d9f04217c2ab71332c08d6ee8659f8b95039df836e141e3d

Observation 90d13969-5966-4fde-9ac7-57bc76824daa · outbound

This paper cites GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints.

Ultra-Sparse Memory Network GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints

Reference 2

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source=arxiv_source observed=2026-08-12T17:40:11.718011Z digest=sha256:273877c1f11e2a06c3ab7a496c91d8b6c481d98c4bad6099b4b37163293dc7ce

Observation 168cae66-14e8-4f63-a909-ba55dd778b30 · outbound

This paper cites Layer Normalization.

Ultra-Sparse Memory Network Layer Normalization

Reference 3

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source=arxiv_source observed=2026-08-12T17:40:11.722221Z digest=sha256:a2a0569ef0158f271b28ee2b431825581686c9b0cdb28a69b4393a2cecba81ea

Observation f6d40994-6891-41eb-9625-ab09f9c52583 · outbound

This paper cites LoTR: Low Tensor Rank Weight Adaptation.

Ultra-Sparse Memory Network LoTR: Low Tensor Rank Weight Adaptation

Reference 4

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source=arxiv_source observed=2026-08-12T17:40:11.725991Z digest=sha256:5c6c80dd0ecb7ab8a3c13271c93178c96d5f82bcc438e8e6b7c5a7b6146a69f1

Observation e02b3f88-803a-4aff-b065-4a1b7452254c · outbound

This paper cites GPT-NeoX-20B: An Open-Source Autoregressive Language Model.

Ultra-Sparse Memory Network GPT-NeoX-20B: An Open-Source Autoregressive Language Model

Reference 5

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source=arxiv_source observed=2026-08-12T17:40:11.730098Z digest=sha256:36dc54a6adfc386784c3e0d3ebcf54d7f7f2c24fc8375a5fc7b9fd4b211745c6

Observation f701ef5f-3caf-4fb8-b0bb-0dc73efae480 · outbound

This paper cites Language Models are Few-Shot Learners.

Ultra-Sparse Memory Network Language Models are Few-Shot Learners

Reference 6

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source=arxiv_source observed=2026-08-12T17:40:11.733772Z digest=sha256:b3ab1e8e5a1ad27a64c22bf3d77bd0fa15bd43ad717631c27efefd4b66b2e6ff

Observation e50a5810-8fa1-4aa3-bb7f-1b75b55a83d0 · outbound

This paper cites On the representation collapse of sparse mixture of experts.

Ultra-Sparse Memory Network On the representation collapse of sparse mixture of experts

Reference 7

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source=arxiv_source observed=2026-08-12T17:40:11.738251Z digest=sha256:2d3ccb868a432db986030789047106b963c3f72ad597580705d3cfdeb260b58d

Observation 9225d8b9-6342-4e42-bf6d-afab1d4231aa · outbound

This paper cites BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions.

Ultra-Sparse Memory Network BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions

Reference 8

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source=arxiv_source observed=2026-08-12T17:40:11.741689Z digest=sha256:e15ec35c9781cc91f9ac7ea9a016b718963e87bf1b6f2ee9df4fbb9a672eb54a

Observation dcba7540-6c9f-485d-a8ba-1bab9920f714 · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

Ultra-Sparse Memory Network Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 9

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source=arxiv_source observed=2026-08-12T17:40:11.745425Z digest=sha256:473ce287811045204fcb6820e143d249e7ce47b91852d52639cb0b858e5517ae

Observation 56d6fdc6-72b9-4807-9a98-95aa69ef0e86 · outbound

This paper cites Redpajama: An open source recipe to reproduce llama training dataset, 2023.

Ultra-Sparse Memory Network Redpajama: An open source recipe to reproduce llama training dataset, 2023

Reference 10

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source=arxiv_source observed=2026-08-12T17:40:11.749017Z digest=sha256:df6e82c42b5312b2beda223eca6425ad5811f7a668ee584823d49cb24b7415d7

Observation c6f7d534-4cca-42e0-88f7-859593b37b3e · outbound

This paper cites Approximating Two-Layer Feedforward Networks for Efficient Transformers.

Ultra-Sparse Memory Network Approximating Two-Layer Feedforward Networks for Efficient Transformers

Reference 11

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source=arxiv_source observed=2026-08-12T17:40:11.752516Z digest=sha256:4c4bd4bf700dd2d39a47c3df0faecd741ddd00b7d9152f7564404a9c462c3278

Observation 793ab649-2840-47a9-8a7f-57020a0cda5c · outbound

This paper cites DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models.

Ultra-Sparse Memory Network DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models

Reference 12

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source=arxiv_source observed=2026-08-12T17:40:11.756344Z digest=sha256:1e303c93ca22a973e6b8a138ce611b7a828014f4dac45d4a6ffd5836b8d164b3

Observation 38f6861d-5427-4df1-8efd-9bfc4aa2b7e4 · outbound

This paper cites DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs.

Ultra-Sparse Memory Network DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs

Reference 13

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source=arxiv_source observed=2026-08-12T17:40:11.759746Z digest=sha256:c5d8a1d2fb656f05a2576449b330bf9cccdc28ad62a860a87563cf83b2fde684

Observation bb380a63-bb88-450b-bef9-61fcf15e1dc0 · outbound

This paper cites The Llama 3 Herd of Models.

Ultra-Sparse Memory Network The Llama 3 Herd of Models

Reference 14

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source=arxiv_source observed=2026-08-12T17:40:11.763763Z digest=sha256:0508c4be6abe2235c0c47c14fcefadb3bfb5a7cd64f498f96dcae3ad0cb04a7b

Observation 837ad763-237e-40b5-86f9-b6359c37b3cc · outbound

This paper cites Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity.

Ultra-Sparse Memory Network Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity

Reference 15

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source=arxiv_source observed=2026-08-12T17:40:11.767356Z digest=sha256:555f863f49c413cf50229702888f63ef4fcfd8b49f57e9ba63a1575b40a55da1

Observation 7cae23e5-68a7-4d02-8b71-e25c2c3a5bab · outbound

This paper cites Transformer Feed-Forward Layers Are Key-Value Memories.

Ultra-Sparse Memory Network Transformer Feed-Forward Layers Are Key-Value Memories

Reference 16

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source=arxiv_source observed=2026-08-12T17:40:11.770687Z digest=sha256:e55fdf3f653769ff406f5a026f7eb282d27c21b4bad0a54972a6bcedd952e6cb

Observation 38ad07e4-8279-44d0-83b1-d549a73128b0 · outbound

This paper cites Mixture of A Million Experts.

Ultra-Sparse Memory Network Mixture of A Million Experts

Reference 17

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source=arxiv_source observed=2026-08-12T17:40:11.774516Z digest=sha256:90a65515ca6e3c67d86cbaf602ec095d62170756f138a0d85df05ab798c394e5

Observation 01cad70a-52e7-4f86-b8b2-86e9405c7870 · outbound

This paper cites Measuring Massive Multitask Language Understanding.

Ultra-Sparse Memory Network Measuring Massive Multitask Language Understanding

Reference 18

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source=arxiv_source observed=2026-08-12T17:40:11.778396Z digest=sha256:40fa2366d89f2b7638757c6ee0c1dc3fd9a56166c22859c79ddecc5330d82b1c

Observation 0bbf8997-344a-4614-988d-05df52dec7fc · outbound

This paper cites MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.

Ultra-Sparse Memory Network MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 19

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source=arxiv_source observed=2026-08-12T17:40:11.782216Z digest=sha256:a58ae5d3c24f7aefe6218cc52aa037c83f16da77e4c769867cf35d5113f35a8e

Observation c98a8dd7-81f7-456d-b2a3-3491cabf162f · outbound

This paper cites Product quantization for nearest neighbor search.

Ultra-Sparse Memory Network Product quantization for nearest neighbor search

Reference 20

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source=arxiv_source observed=2026-08-12T17:40:11.785609Z digest=sha256:85e9682e2f0252052f09613c5b44739aac1d82847d306897bb0b27234dffdb56

Observation cf2fe7cc-3dc0-41af-be00-b9f54fccc2b7 · outbound

This paper cites Mixtral of Experts.

Ultra-Sparse Memory Network Mixtral of Experts

Reference 21

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source=arxiv_source observed=2026-08-12T17:40:11.788900Z digest=sha256:40dacdd9990c4625122f857d6aeaf44c72ede9b044c771448db92e71e8346c65

Observation f5b6a5c7-2924-4699-b298-f3874bedbd2a · outbound

This paper cites TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension.

Ultra-Sparse Memory Network TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension

Reference 22

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source=arxiv_source observed=2026-08-12T17:40:11.792408Z digest=sha256:382d29365bf3882b6a2d62560bf2278af703ebe32a15733cdb6dcd45bba62a4e

Observation eef2b7ce-bc41-4ad5-b63d-54980cf762fe · outbound

This paper cites Large Product Key Memory for Pretrained Language Models.

Ultra-Sparse Memory Network Large Product Key Memory for Pretrained Language Models

Reference 23

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source=arxiv_source observed=2026-08-12T17:40:11.796041Z digest=sha256:331b237af7b309d52402fbdc7ac650c49fc32787e3e5d7bdad977898cb60b6ab

Observation 55220a9d-b5c6-428f-b64d-973a86cb10ba · outbound

This paper cites Scaling Laws for Fine-Grained Mixture of Experts.

Ultra-Sparse Memory Network Scaling Laws for Fine-Grained Mixture of Experts

Reference 24

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source=arxiv_source observed=2026-08-12T17:40:11.799681Z digest=sha256:92e6618923e87d4c2346aa0daf18b19ca73c4960ee8c7240dd4dc5a94a292c9e

Observation e15c0b11-7d9b-4a29-9f09-9ae6a723a9e6 · outbound

This paper cites Large memory layers with product keys.

Ultra-Sparse Memory Network Large memory layers with product keys

Reference 25

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source=arxiv_source observed=2026-08-12T17:40:11.803785Z digest=sha256:bfb80ea8a8efec05a3ce568d8fc110282a074fbb168f077833967ae391aecd88

Observation 7b815b85-e9ab-4701-9624-96aa1d4dbaf3 · outbound

This paper cites DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model.

Ultra-Sparse Memory Network DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model

Reference 26

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source=arxiv_source observed=2026-08-12T17:40:11.807604Z digest=sha256:4af600549c1700f4902037d257ca317da0f23207286270a4bc254e0b2633546b

Observation c08f18c0-0d65-408f-a388-7880799ec553 · outbound

This paper cites Low-rank tucker decomposition of large tensors using tensorsketch.

Ultra-Sparse Memory Network Low-rank tucker decomposition of large tensors using tensorsketch

Reference 27

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source=arxiv_source observed=2026-08-12T17:40:11.811038Z digest=sha256:0e26de7784f9e05d71213974f6f57a867be4d11f8f6515938b93d963190dc2cf

Observation a522919e-9bde-4b9d-a226-9c561f661c50 · outbound

This paper cites Efficient large-scale language model training on gpu clusters using megatron-lm.

Ultra-Sparse Memory Network Efficient large-scale language model training on gpu clusters using megatron-lm

Reference 28

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source=arxiv_source observed=2026-08-12T17:40:11.814523Z digest=sha256:78cc0dc229d9e2f5d845f94537fe3a3641d478f109b2b18918663d9cf263d2e7

Observation ceec7ec9-9da3-4730-aa58-35fd6a9fada6 · outbound

This paper cites Language models are unsupervised multitask learners.

Ultra-Sparse Memory Network Language models are unsupervised multitask learners

Reference 29

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source=arxiv_source observed=2026-08-12T17:40:11.818131Z digest=sha256:78e9522b6927e393d27ac9ad5bb5cf638fd84da70c9059e8afab664d8fb72ef3

Observation fe2f46d1-93fb-4e1b-a590-a890f30b35b8 · outbound

This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer.

Ultra-Sparse Memory Network Exploring the limits of transfer learning with a unified text-to-text transformer

Reference 30

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source=arxiv_source observed=2026-08-12T17:40:11.821670Z digest=sha256:7a93e02d32c859e99cb90741a7e07bea41517d0717ae25173fa72d9f1e97cc49

Observation 5098089c-cb9e-413e-9e15-15daad3e080b · outbound

This paper cites Deepspeed-moe: Advancing mixture-of-experts inference and training to power next-generation ai scale.

Ultra-Sparse Memory Network Deepspeed-moe: Advancing mixture-of-experts inference and training to power next-generation ai scale

Reference 31

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source=arxiv_source observed=2026-08-12T17:40:11.825104Z digest=sha256:9919289f914a7a5fef6fb3d285b45e8391ce0c223a8bcbe5cf6c89628d0a5152

Observation a5b61079-0425-4b16-b1ea-b59d91fae9c9 · outbound

This paper cites GPQA: A Graduate-Level Google-Proof Q&A Benchmark.

Ultra-Sparse Memory Network GPQA: A Graduate-Level Google-Proof Q&A Benchmark

Reference 32

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source=arxiv_source observed=2026-08-12T17:40:11.828614Z digest=sha256:e16896ecb262234a791179e5a621f320e84f627803dd04b23d0c6901b3a80fcd

Observation 7991625d-3965-4cba-8660-45da45170c0b · outbound

This paper cites Hash layers for large sparse models.

Ultra-Sparse Memory Network Hash layers for large sparse models

Reference 33

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source=arxiv_source observed=2026-08-12T17:40:11.832312Z digest=sha256:52dc31f466944a14820762b1654feaf4832edfa99cee6b871d99cd7a1efbc4b3

Observation 417f0688-3ddf-4135-bc75-1ce52165397e · outbound

This paper cites Winogrande: An adversarial winograd schema challenge at scale.

Ultra-Sparse Memory Network Winogrande: An adversarial winograd schema challenge at scale

Reference 34

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source=arxiv_source observed=2026-08-12T17:40:11.835946Z digest=sha256:76be364f270b2c95b5106f9d413422c4e65df1ae815554215c850339b01f370d

Observation 3dda8bfd-b93f-4cd4-acdc-4f3e3cbdf1ec · outbound

This paper cites Neural Machine Translation of Rare Words with Subword Units.

Ultra-Sparse Memory Network Neural Machine Translation of Rare Words with Subword Units

Reference 35

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source=arxiv_source observed=2026-08-12T17:40:11.839323Z digest=sha256:a74753eaa95e15a80a1710ab47602b4997cd786d957b71ab5d64610553311c83

Observation f6eeeeeb-0339-4fe8-915d-adba2ce6b7c8 · outbound

This paper cites Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer.

Ultra-Sparse Memory Network Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Reference 36

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source=arxiv_source observed=2026-08-12T17:40:11.843128Z digest=sha256:79bffb6d5deb0a4827ba047c7d497581c916a3700d5a17c0e8f0eaf83366b16e

Observation 4178765d-a19c-4b53-a8a4-42790ac2b85a · outbound

This paper cites A Study on ReLU and Softmax in Transformer.

Ultra-Sparse Memory Network A Study on ReLU and Softmax in Transformer

Reference 37

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source=arxiv_source observed=2026-08-12T17:40:11.846944Z digest=sha256:ec51f50b9b925734265008406635f4371a7ddb15f3345921de7950f0227040d3

Observation b268acff-ebab-4aa8-a23e-1dc64dfbd0e8 · outbound

This paper cites Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism.

Ultra-Sparse Memory Network Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism

Reference 38

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source=arxiv_source observed=2026-08-12T17:40:11.850766Z digest=sha256:03ac225bb70dc7e329ca88e06684dffd85a9940179e978c5db655f8f608e5ec3

Observation d8b17f34-6df3-4d25-96fd-30cbe79577db · outbound

This paper cites Roformer: Enhanced transformer with rotary position embedding.

Ultra-Sparse Memory Network Roformer: Enhanced transformer with rotary position embedding

Reference 39

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source=arxiv_source observed=2026-08-12T17:40:11.854624Z digest=sha256:b54fd2555ca8d2af5e1102dbb9c95837d4f9504801ebfc8ba41e3b3626a36a70

Observation 2798ba0c-1687-4752-802c-a3c2dcf5fba7 · outbound

This paper cites Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them.

Ultra-Sparse Memory Network Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 40

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source=arxiv_source observed=2026-08-12T17:40:11.858088Z digest=sha256:2185c7fcaa61ff4734fd3b382cff9ea74a7a43006e7334ba5248daf5c07f8da1

Observation a8de3515-0ce9-4973-8b3e-a78c8e442842 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Ultra-Sparse Memory Network LLaMA: Open and Efficient Foundation Language Models

Reference 41

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source=arxiv_source observed=2026-08-12T17:40:11.862189Z digest=sha256:c6ace4791386b56f5a170bb0921ea2e9b9db0ad5bbc1a659ed13a95743628e69

Observation d430c2f1-83cc-4949-a235-da421349aa98 · outbound

This paper cites On layer normalization in the transformer architecture.

Ultra-Sparse Memory Network On layer normalization in the transformer architecture

Reference 42

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no resolver link, observed 2026-08-12T17:40:11.866101Z

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source=arxiv_source observed=2026-08-12T17:40:11.866101Z digest=sha256:62349b5c21b8e4f46929a0ec93128eb121078ddcdec5d71b14886c2a6943b41e

Observation 1bd81ccb-27cc-4159-b932-60140161e6cb · outbound

This paper cites HellaSwag: Can a Machine Really Finish Your Sentence?.

Ultra-Sparse Memory Network HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 43

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no resolver link, observed 2026-08-12T17:40:11.869601Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-12T17:40:11.869601Z digest=sha256:c1a0ddb7c8052af660a49a319145d18f1c2cf5f27f80daab9ec981ffbba601e4

Observation f964a509-040d-4db2-8c1f-3416831f6264 · outbound

This paper cites AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models.

Ultra-Sparse Memory Network AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models

Reference 44

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no resolver link, observed 2026-08-12T17:40:11.873164Z

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source=arxiv_source observed=2026-08-12T17:40:11.873164Z digest=sha256:0c05c9014619857f94c859a472d050f20b401e01fe97721ebab2f62c34176271

Observation a166d638-22f7-4ea9-8a54-d8577b75451c · outbound

This paper cites Mixture-of-experts with expert choice routing.

Ultra-Sparse Memory Network Mixture-of-experts with expert choice routing

Reference 45

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no resolver link, observed 2026-08-12T17:40:11.876728Z

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source=arxiv_source observed=2026-08-12T17:40:11.876728Z digest=sha256:92e33dde1a113f074c7c2ae305c16cb9812132ca6a381608c2021764b928a444

Observation 57c341e5-e6ae-4d34-b417-20149107f429 · outbound

This paper cites write newline.

Ultra-Sparse Memory Network write newline

Reference 46

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Source-reported events for the cited work

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source=arxiv_source observed=2026-08-12T17:40:11.880130Z digest=sha256:f66b7f6b08b042b1376b407a9ca6efa927123efc44b7bdf60480482b01fe8999

Pith citing papers

Observation 2b074278-78de-4f66-a8ea-d8d8537c3e3c · inbound

ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing cites this paper.

ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing Ultra-Sparse Memory Network

Reference 17

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no resolver link, observed 2026-08-11T12:03:42.481591Z

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source=arxiv_source observed=2026-08-11T12:03:42.481591Z digest=sha256:be961cd9782f0ea1a393bd2d9498697b893c1f2ef300d13a296b62a72d518efb

Observation ab689d32-1985-4586-bc5b-1776e1733e0c · inbound

Unveiling Instruction-Specific Neurons & Experts: An Analytical Framework for LLM's Instruction-Following Capabilities cites this paper.

Unveiling Instruction-Specific Neurons & Experts: An Analytical Framework for LLM's Instruction-Following Capabilities Ultra-Sparse Memory Network

Reference 12

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no resolver link, observed 2026-08-07T13:43:20.892077Z

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source=arxiv_source observed=2026-08-07T13:43:20.892077Z digest=sha256:a8f24b0dca224e705af720f7d9b14d933326624f34fc5aa7fe84a48bfee17e7b

Observation e8ecbb92-aa3c-432f-9a7c-a9f8c04ef171 · inbound

UltraMemV2: Memory Networks Scaling to 120B Parameters with Superior Long-Context Learning cites this paper.

UltraMemV2: Memory Networks Scaling to 120B Parameters with Superior Long-Context Learning Ultra-Sparse Memory Network

Reference 18

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no resolver link, observed 2026-08-05T16:17:41.998354Z

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source=pdf_text observed=2026-08-05T16:17:41.998354Z digest=sha256:52032437cef4b8e262ed4893f138f84186fcf8e69ad1381236179b2704440169

Observation 9a4381f8-292a-496d-8f4e-52436d4de2cd · inbound

MIDUS: Memory-Infused Depth Up-Scaling cites this paper.

MIDUS: Memory-Infused Depth Up-Scaling Ultra-Sparse Memory Network

Reference 11

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verified exact
arxiv_id, observed 2026-05-16T22:03:36.155671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-16T22:02:42.297041Z digest=sha256:33d9ef794550464c0b1139da95b9f56aaf14651ac61dc805e39fd3d0d1287285

Observation 05776b31-66e4-4652-bfcc-c8b9859efb31 · inbound

Memory Grafting: Scaling Language Model Pre-training via Offline Conditional Memory cites this paper.

Memory Grafting: Scaling Language Model Pre-training via Offline Conditional Memory Ultra-Sparse Memory Network

Reference 21

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verified exact
arxiv_id, observed 2026-05-21T05:49:40.770806Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-21T05:49:14.789955Z digest=sha256:be790a9a737fa5753b768a9e4eaef89574ca5111f4f8439e0d23d0d007d24469

Observation 4b42034b-fa73-4903-833d-76720aa0da34 · inbound

SinkRec: Mitigating Semantic State Sink in Long Sequence Recommendation with Memory-Conditioned Gated Delta Networks cites this paper.

SinkRec: Mitigating Semantic State Sink in Long Sequence Recommendation with Memory-Conditioned Gated Delta Networks Ultra-Sparse Memory Network

Reference 11

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verified exact
arxiv_id, observed 2026-07-02T07:06:43.815473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-06-28T07:13:54.031036Z digest=sha256:436d72df0c05fceec4decff7b72a02caa13b39b3c988f50aa32097dfc87b3da3

Observation 07965896-2fa1-4712-ae28-18f94b5ae216 · inbound

Augmenting Molecular Language Models with Local $n$-gram Memory cites this paper.

Augmenting Molecular Language Models with Local $n$-gram Memory Ultra-Sparse Memory Network

Reference 28

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metadata mismatch
arxiv_id, observed 2026-07-03T10:37:56.681243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-06-27T09:57:12.398344Z digest=sha256:dea2cb85bca47623bd7af77947d242b9486cc1be753bc9bc1e30f9d98c50736c

Observation 3ef7cfa3-82d5-4c3d-af11-1ef9450c970d · inbound

User as Engram: Internalizing Per-User Memory as Local Parametric Edits cites this paper.

User as Engram: Internalizing Per-User Memory as Local Parametric Edits Ultra-Sparse Memory Network

Reference 38

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verified exact
arxiv_id, observed 2026-07-04T01:09:19.455849Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-06-26T20:37:01.382431Z digest=sha256:187f0d887419254e268120384ce248bcd6dda77109a9992b06af4c9fe80121d7

Observation e364a06c-7f2d-4cab-aeaf-bc5df9d406ab · inbound

Train Smarter, Not Longer: Memorization-Guided Data Reuse for Efficient LLM Training cites this paper.

Train Smarter, Not Longer: Memorization-Guided Data Reuse for Efficient LLM Training Ultra-Sparse Memory Network

Reference 7

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no resolver link, observed 2026-07-11T10:45:46.618668Z

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source=pdf_text observed=2026-07-11T10:45:46.618668Z digest=sha256:941a4ccd0ef017f4ec3a720aa5a25631f2f715b24a6a96e31261c9740dc7b0b4